Laplacian eigenvalues of the second power of a graph

the impact of training on second language writing assessment: a case of raters’ biasedness

چکیده هدف اول این تحقیق بررسی تأثیر آموزش مصحح بر آموزش گیرندگان براساس پایایی نمره های آنها در پنج بخش شامل محتوا ، سازمان ، لغت ، زبان و مکانیک بود. هدف دوم این بود که بدانیم آیا تفاوتهای بین آموزشی گیرندگان زن و مرد در پایایی نمرات آنها وجود دارد. برای بررسی این موارد ، ما 90 دانشجو در سطح میانه (متوسط) که از طریق تست تعیین سطح شده بودند انتخاب شدند. بعد از آنها خواستیم که درباره دو موضوع ا...

The Main Eigenvalues of the Undirected Power Graph of a Group

The undirected power graph of a finite group $G$, $P(G)$, is a graph with the group elements of $G$ as vertices and two vertices are adjacent if and only if one of them is a power of the other. Let $A$ be an adjacency matrix of $P(G)$. An eigenvalue $lambda$ of $A$ is a main eigenvalue if the eigenspace $epsilon(lambda)$ has an eigenvector $X$ such that $X^{t}jjneq 0$, where $jj$ is the all-one...

Laplacian eigenvalues and optimality: II. The Laplacian of a graph

On Laplacian Eigenvalues of a Graph

Let G be a connected graph with n vertices and m edges. The Laplacian eigenvalues are denoted by μ1(G) ≥ μ2(G) ≥ ·· · ≥ μn−1(G) > μn(G) = 0. The Laplacian eigenvalues have important applications in theoretical chemistry. We present upper bounds for μ1(G)+ · · ·+μk(G) and lower bounds for μn−1(G)+ · · ·+μn−k(G) in terms of n and m, where 1 ≤ k ≤ n−2, and characterize the extremal cases. We also ...

Graph Embeddings and Laplacian Eigenvalues

Graph embeddings are useful in bounding the smallest nontrivial eigenvalues of Laplacian matrices from below. For an n×n Laplacian, these embedding methods can be characterized as follows: The lower bound is based on a clique embedding into the underlying graph of the Laplacian. An embedding can be represented by a matrix Γ; the best possible bound based on this embedding is n/λmax(Γ Γ). Howeve...